backbone_norm_cfg = dict(type="LN", requires_grad=True)
norm_cfg = dict(type="BN", requires_grad=True)
angle_version = "le90"

model = dict(
    type="OrientedRCNN",
    backbone=dict(
        type="DenSeAdViT",
        img_size=512,
        patch_size=16,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4,
        qkv_bias=True,
        pruning_loc=[2, 4, 6],
        drop_path_rate=0.2,
        token_ratio=[0.7, 0.7**2, 0.7**3],
        distill=True,
    ),
    neck=dict(
        type="SimpleFPN",
        backbone_channel=768,
        in_channels=[192, 384, 768, 768],
        out_channels=256,
        num_outs=5,
        norm_cfg=norm_cfg,
    ),
    rpn_head=dict(
        type="OrientedRPNHead",
        in_channels=256,
        feat_channels=256,
        version=angle_version,
        anchor_generator=dict(
            type="AnchorGenerator",
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64],
        ),
        bbox_coder=dict(
            type="MidpointOffsetCoder",
            angle_range=angle_version,
            target_means=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0, 0.5, 0.5],
        ),
        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type="SmoothL1Loss", beta=0.1111111111111111, loss_weight=1.0),
    ),
    roi_head=dict(
        type="OrientedStandardRoIHead",
        bbox_roi_extractor=dict(
            type="RotatedSingleRoIExtractor",
            roi_layer=dict(
                type="RoIAlignRotated", out_size=7, sample_num=2, clockwise=True
            ),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32],
        ),
        bbox_head=dict(
            type="RotatedShared2FCBBoxHead",
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=1,
            bbox_coder=dict(
                type="DeltaXYWHAOBBoxCoder",
                angle_range=angle_version,
                norm_factor=None,
                edge_swap=True,
                proj_xy=True,
                target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
                target_stds=(0.1, 0.1, 0.2, 0.2, 0.1),
            ),
            reg_class_agnostic=True,
            loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.0),
        ),
    ),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type="MaxIoUAssigner",
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1,
            ),
            sampler=dict(
                type="RandomSampler",
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False,
            ),
            allowed_border=0,
            pos_weight=-1,
            debug=False,
        ),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type="nms", iou_threshold=0.8),
            min_bbox_size=0,
        ),
        rcnn=dict(
            assigner=dict(
                type="MaxIoUAssigner",
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                iou_calculator=dict(type="RBboxOverlaps2D"),
                ignore_iof_thr=-1,
            ),
            sampler=dict(
                type="RRandomSampler",
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True,
            ),
            pos_weight=-1,
            debug=False,
        ),
    ),
    test_cfg=dict(
        rpn=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type="nms", iou_threshold=0.8),
            min_bbox_size=0,
        ),
        rcnn=dict(
            nms_pre=2000,
            min_bbox_size=0,
            score_thr=0.05,
            nms=dict(iou_thr=0.1),
            max_per_img=2000,
        ),
    ),
)
